Quantum neural networks facilitating quantum state classification

• URL: http://arxiv.org/abs/2504.06622v1
• Date: Wed, 09 Apr 2025 06:42:32 GMT
• Title: Quantum neural networks facilitating quantum state classification
• Authors: Diksha Sharma, Vivek Balasaheb Sabale, Thirumalai M., Atul Kumar,
• Abstract summary: This work establishes a foundation for the classification of multi-qubit quantum states.<n>It offers the potential for generalisation to multi-qubit pure quantum states.
• Score: 1.3187011661009458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The classification of quantum states into distinct classes poses a significant challenge. In this study, we address this problem using quantum neural networks in combination with a problem-inspired circuit and customised as well as predefined ans\"{a}tz. To facilitate the resource-efficient quantum state classification, we construct the dataset of quantum states using the proposed problem-inspired circuit. The problem-inspired circuit incorporates two-qubit parameterised unitary gates of varying entangling power, which is further integrated with the ans\"{a}tz, developing an entire quantum neural network. To demonstrate the capability of the selected ans\"{a}tz, we visualise the mitigated barren plateaus. The designed quantum neural network demonstrates the efficiency in binary and multi-class classification tasks. This work establishes a foundation for the classification of multi-qubit quantum states and offers the potential for generalisation to multi-qubit pure quantum states.

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